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- # copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- import numpy as np
- from ..base import BaseComponent
- __all__ = ["TableLabelDecode"]
- class TableLabelDecode(BaseComponent):
- """decode the table model outputs(probs) to character str"""
- ENABLE_BATCH = True
- INPUT_KEYS = ["pred", "img_size", "ori_img_size"]
- OUTPUT_KEYS = ["bbox", "structure", "structure_score"]
- DEAULT_INPUTS = {
- "pred": "pred",
- "img_size": "img_size",
- "ori_img_size": "ori_img_size",
- }
- DEAULT_OUTPUTS = {
- "bbox": "bbox",
- "structure": "structure",
- "structure_score": "structure_score",
- }
- def __init__(self, model_name, merge_no_span_structure=True, dict_character=[]):
- super().__init__()
- if merge_no_span_structure:
- if "<td></td>" not in dict_character:
- dict_character.append("<td></td>")
- if "<td>" in dict_character:
- dict_character.remove("<td>")
- self.model_name = model_name
- dict_character = self.add_special_char(dict_character)
- self.dict = {}
- for i, char in enumerate(dict_character):
- self.dict[char] = i
- self.character = dict_character
- self.td_token = ["<td>", "<td", "<td></td>"]
- def add_special_char(self, dict_character):
- """add_special_char"""
- self.beg_str = "sos"
- self.end_str = "eos"
- dict_character = dict_character
- dict_character = [self.beg_str] + dict_character + [self.end_str]
- return dict_character
- def get_ignored_tokens(self):
- """get_ignored_tokens"""
- beg_idx = self.get_beg_end_flag_idx("beg")
- end_idx = self.get_beg_end_flag_idx("end")
- return [beg_idx, end_idx]
- def get_beg_end_flag_idx(self, beg_or_end):
- """get_beg_end_flag_idx"""
- if beg_or_end == "beg":
- idx = np.array(self.dict[self.beg_str])
- elif beg_or_end == "end":
- idx = np.array(self.dict[self.end_str])
- else:
- assert False, "unsupported type %s in get_beg_end_flag_idx" % beg_or_end
- return idx
- def apply(self, pred, img_size, ori_img_size):
- """apply"""
- bbox_preds, structure_probs = [], []
- for bbox_pred, stru_prob in pred:
- bbox_preds.append(bbox_pred)
- structure_probs.append(stru_prob)
- bbox_preds = np.array(bbox_preds)
- structure_probs = np.array(structure_probs)
- bbox_list, structure_str_list, structure_score = self.decode(
- structure_probs, bbox_preds, img_size, ori_img_size
- )
- structure_str_list = [
- (
- ["<html>", "<body>", "<table>"]
- + structure
- + ["</table>", "</body>", "</html>"]
- )
- for structure in structure_str_list
- ]
- return [
- {"bbox": bbox, "structure": structure, "structure_score": structure_score}
- for bbox, structure in zip(bbox_list, structure_str_list)
- ]
- def decode(self, structure_probs, bbox_preds, padding_size, ori_img_size):
- """convert text-label into text-index."""
- ignored_tokens = self.get_ignored_tokens()
- end_idx = self.dict[self.end_str]
- structure_idx = structure_probs.argmax(axis=2)
- structure_probs = structure_probs.max(axis=2)
- structure_batch_list = []
- bbox_batch_list = []
- batch_size = len(structure_idx)
- for batch_idx in range(batch_size):
- structure_list = []
- bbox_list = []
- score_list = []
- for idx in range(len(structure_idx[batch_idx])):
- char_idx = int(structure_idx[batch_idx][idx])
- if idx > 0 and char_idx == end_idx:
- break
- if char_idx in ignored_tokens:
- continue
- text = self.character[char_idx]
- if text in self.td_token:
- bbox = bbox_preds[batch_idx, idx]
- bbox = self._bbox_decode(
- bbox, padding_size[batch_idx], ori_img_size[batch_idx]
- )
- bbox_list.append(bbox.astype(int))
- structure_list.append(text)
- score_list.append(structure_probs[batch_idx, idx])
- structure_batch_list.append(structure_list)
- structure_score = np.mean(score_list)
- bbox_batch_list.append(bbox_list)
- return bbox_batch_list, structure_batch_list, structure_score
- def decode_label(self, batch):
- """convert text-label into text-index."""
- structure_idx = batch[1]
- gt_bbox_list = batch[2]
- shape_list = batch[-1]
- ignored_tokens = self.get_ignored_tokens()
- end_idx = self.dict[self.end_str]
- structure_batch_list = []
- bbox_batch_list = []
- batch_size = len(structure_idx)
- for batch_idx in range(batch_size):
- structure_list = []
- bbox_list = []
- for idx in range(len(structure_idx[batch_idx])):
- char_idx = int(structure_idx[batch_idx][idx])
- if idx > 0 and char_idx == end_idx:
- break
- if char_idx in ignored_tokens:
- continue
- structure_list.append(self.character[char_idx])
- bbox = gt_bbox_list[batch_idx][idx]
- if bbox.sum() != 0:
- bbox = self._bbox_decode(bbox, shape_list[batch_idx])
- bbox_list.append(bbox.astype(int))
- structure_batch_list.append(structure_list)
- bbox_batch_list.append(bbox_list)
- return bbox_batch_list, structure_batch_list
- def _bbox_decode(self, bbox, padding_shape, ori_shape):
- if self.model_name == "SLANet":
- w, h = ori_shape
- bbox[0::2] *= w
- bbox[1::2] *= h
- else:
- w, h = padding_shape
- ori_w, ori_h = ori_shape
- ratio_w = w / ori_w
- ratio_h = h / ori_h
- ratio = min(ratio_w, ratio_h)
- bbox[0::2] *= w
- bbox[1::2] *= h
- bbox[0::2] /= ratio
- bbox[1::2] /= ratio
- return bbox
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